Sciweavers

149
Voted
DATAMINE
2007
135views more  DATAMINE 2007»
15 years 22 days ago
Experiencing SAX: a novel symbolic representation of time series
Many high level representations of time series have been proposed for data mining, including Fourier transforms, wavelets, eigenwaves, piecewise polynomial models etc. Many researc...
Jessica Lin, Eamonn J. Keogh, Li Wei, Stefano Lona...
56
Voted
DATAMINE
2007
85views more  DATAMINE 2007»
15 years 22 days ago
Genetic process mining: an experimental evaluation
Ana Karla A. de Medeiros, A. J. M. M. Weijters, Wi...
102
Voted
DATAMINE
2007
101views more  DATAMINE 2007»
15 years 22 days ago
Using metarules to organize and group discovered association rules
The high dimensionality of massive data results in the discovery of a large number of association rules. The huge number of rules makes it difficult to interpret and react to all ...
Abdelaziz Berrado, George C. Runger
69
Voted
DATAMINE
2007
79views more  DATAMINE 2007»
15 years 22 days ago
Locally adaptive metrics for clustering high dimensional data
Carlotta Domeniconi, Dimitrios Gunopulos, Sheng Ma...
58
Voted
DATAMINE
2007
62views more  DATAMINE 2007»
15 years 22 days ago
Privacy-preserving boosting
Sébastien Gambs, Balázs Kégl,...
87
Voted
DATAMINE
2007
110views more  DATAMINE 2007»
15 years 22 days ago
The complexity of non-hierarchical clustering with instance and cluster level constraints
Recent work has looked at extending clustering algorithms with instance level must-link (ML) and cannot-link (CL) background information. Our work introduces δ and ǫ cluster lev...
Ian Davidson, S. S. Ravi
64
Voted
DATAMINE
2007
84views more  DATAMINE 2007»
15 years 22 days ago
Compression-based data mining of sequential data
Eamonn J. Keogh, Stefano Lonardi, Chotirat Ann Rat...
61
Voted
DATAMINE
2007
53views more  DATAMINE 2007»
15 years 22 days ago
Non-derivable itemset mining
Toon Calders, Bart Goethals
72
Voted
DATAMINE
2007
93views more  DATAMINE 2007»
15 years 22 days ago
Evaluation of ordinal attributes at value level
We propose a novel context sensitive algorithm for evaluation of ordinal attributes which exploits the information hidden in ordering of attributes’ and class’ values and prov...
Marko Robnik-Sikonja, Koen Vanhoof